EPID-20. NOVEL GLIOBLASTOMA POPULATION-BASED HISTOLOGIC STAIN NORMALIZATION

Abstract

Abstract Histopathologic evaluation has been an integral part of clinical diagnosis for central nervous system tumors, providing information essential for classification, management, and treatment of the disease. Hematoxylin and eosin (H&E) staining is routinely used in histology, providing detail of tissue morphology, structure, and cellular composition. MOTIVATION: Slide staining is rife with color intensity variations, mainly due to differences in materials and staining protocols among others. These variations introduce inaccuracies in downstream computational analysis and quantification of disease, disabling the generalization of computational models. To overcome these variations, current approaches arbitrarily select a slide within the cohort to normalize all slides of the cohort, leading to non-reproducible results in other cohorts. We develop a population-based whole slide image (WSI) normalization method based on overall region driven stain vectors and color histogram, weighted by corresponding percent contribution to overall slide (PCOS). METHODS: We identified a cohort of 509 H&E stained WSIs with corresponding anatomical annotations from the Ivy Glioblastoma Atlas Project. These WSIs and annotations were reviewed by two neuropathologists for correctly annotated regions. Each region was weighted according to PCOS, WSIs with PCOS < 0.05% were discarded. Then, the optical densities and histograms calculated. Resulting color histogram and optical density was applied to the WSI cohort. Finally, stain intensity variability pre- and post- normalization was compared. RESULTS: Normalizing WSIs based on our approach, results in a significant (p < 0.01, Wilcoxon) improvement in color intensity variation for eight of nine regions tested, with the exception of “Pseudopalisading Cells with no visible Necrosis” (p = 0.8). DISCUSSION: This novel transformative technique is insensitive to artificially staining background density and straightforward to apply. Furthermore, the approach shows promise towards a viable and robust tool for stain normalization in large WSIs cohorts, with the potential towards a stain normalization standard generalizable to other diseases.